GPU Accelerated Image Quality Assessment-Based Software for Transient Detection

TL;DR

This paper presents GPU-accelerated software for transient detection in astronomical images, leveraging IQA methods to handle the high data throughput of next-generation telescopes like SKA.

Contribution

It introduces GPU-based implementations of IQA methods for fast transient detection, significantly reducing processing time for large images.

Findings

Kernel time of ~0.1 ms for 2048x2048 images

Effective acceleration of IQA-based transient finders using GPUs

Potential for real-time transient detection in large-scale radio astronomy

Abstract

Fast imaging localises celestial transients using source finders in the image domain. The need for high computational throughput in this process is driven by next-generation telescopes such as Square Kilometre Array (SKA), which, upon completion, will be the world's largest aperture synthesis radio telescope. It will collect data at unprecedented velocity and volume. Due to the vast amounts of data the SKA will produce, current source finders based on source extraction may be inefficient in a wide-field search. In this paper, we focus on the software development of GPU-accelerated transient finders based on Image Quality Assessment (IQA) methods -- Low-Information Similarity Index (LISI) and augmented LISI (augLISI). We accelerate the algorithms using GPUs, achieving kernel time of approximately 0.1 milliseconds for transient finding in 2048X2048 images.